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We also assessed whether the motif detection tools could recover motifs in datasets with lower signal to noise levels e.g. by searching for a low IC motif (Figure 2(B)) or by searching for a high IC motif in a dataset where not all sequences contain a motif instance (Figure 2(C)).

This is also the approach that the pipeline motif detection tools implement which we discussed in the manuscript.

There are a number of publicly available network motif detection tools, namely MFINDER [ 13], MAVISTO [ 41], FANMOD [ 42], NetMatch [ 43], and SNAVI [ 44].

In another study, computational methods have first been used to identify modules of co-regulated Pf genes, and motif detection tools have then been used to identify potential TFBMs in these modules [ 18].

In contrast to the motif detection tools MAVisto [ 61] and Mfinder [ 36]), our implementation in Prolog and the FANMOD [ 62] motif finding tool are able to find any annotated network pattern consisting of any number of nodes and edges.

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We used the network motif detection tool FANMOD FAst Network MOtiff Detection) 20 for detecting motifs in all networks in Table 1.

Motif detection tool FANMOD was used to detect all possible three-node and four-node motifs in each of the conditional networks [ 23].

The 'performance measures' describe whether the motif detection tool is able to retrieve the motif model of the embedded motif in a particular test.

To further enhance prediction quality, we applied our new de novo motif detection tool MHMMotif to intronic ends and exons.

The fast network motif detection tool, FANMOD, created by Rasche and Wernicke, uses an algorithm called RAND-ESU in order to enumerate and sample subgraphs in given networks.

We then applied the de novo motif detection tool MEME [ 24] on the sequences within ± 100 bp of the summits of the 1357 LXR peaks with a FDR < 1%.

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